基于EMD和MLEM2的滚动轴承智能故障诊断方法

针对旋转机械的自主故障诊断，提出一种基于EMD和MLEM2的智能解决方法。利用EMD预处理振动信号，在最适合的IMF分量上提取6个时域指标和5个频域指标构成无量纲的轴承故障特征向量。根据设备运行数据形成决策表，使用改进的MLEM2算法挖掘诊断规则，再结合改进的规则匹配策略进行状态识别。EMD能够剥离故障最本质的信息，提高所选分量的信噪比，而MLEM2算法无需对连续属性事先离散化，获得的诊断规则更完备、准确。SKF6203轴承试验表明，该方法诊断精度达到93.75%，相当于能够自主获取知识的专家系统，且只要一次初始设定，无需后续人工干预，是一种有效的智能诊断方法。

Intelligent fault diagnosis method for rolling bearings based on EMD and MLEM2

To solve the problems of automatic fault diagnosis of rotating machinery, an intelligent method based on EMD and MLEM2 was presented. EMD was used to preprocess the original vibration signal, after that, six time-domain characteristic indices and five frequency-domain indices were calculated on the most appropriate IMF to form the dimensionless fault eigenvector of rolling bearings. According to the characteristic vector, fault decision table could be acquired by the data collected from the running machine. The MLEM2 algorithm was then applied to mine diagnostic rules from the data table. By these rules and an improved rule matching strategy, the final fault classification was carried out. EMD could discover the fault essence of the signal, and enhance the signal-to-noise rate of the selected IMF, while MLEM2 algorithm could be operated without attribute discretization, so the result rules were more complete and accurate. It was proved by the experiment of SKF6203 rolling bearings that the accuracy of this method reached 93.75%. It works like an expert system with the ability of acquiring knowledge itself, and does not need any artificial interference once the initialization is made. It is a valid method for intelligent fault diagnosis of rolling bearings.